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    The statistical analysis of the relationship between particulate matter with traffic and meteorological parameters
    (Işık Üniversitesi, 2023-06-07) Panhwar, Mehran; Kesten, Ali Sercan; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, İnşaat Mühendisliği Yüksek Lisans Programı
    Particulate matter (PM) pollution has become a pressing concern due to its detrimental effects on human health and the environment. Understanding the relationship between PM and meteorological parameters, as well as the impact of traffic, is crucial for effective pollution control strategies. This thesis aims to analyze these relationships by employing an Ordinary Least Squares (OLS) regression model for PM1.0, PM2.5, and PM10 concentrations. A comprehensive dataset of PM measurements, meteorological data, and traffic-related variables is collected from various monitoring stations over a specific time period. Meteorological parameters such as temperature and wind speed, are obtained from corresponding meteorological stations, while traffic data includes vehicle counts and road characteristics. The initial analysis reveals significant associations between PM concentrations, meteorological parameters, and traffic impact. The OLS regression model is used to investigate the individual and combined effects of meteorological variables and traffic on PM levels. The results show that temperature, highway traffic and wind speed changes the PM concentrations, indicating that higher temperatures and traffic contribute to increased PM1.0, PM2.5, and PM10 levels. Wind speed demonstrates a negative correlation, suggesting that higher wind speeds are associated with lower PM concentrations due to dispersion effects. Furthermore, the model reveals that traffic-related variables, significantly influence PM pollution, with increased traffic leading to higher PM concentrations. The findings of this study provide valuable insights into the complex relationships between PM pollution, meteorological parameters, and traffic impact. These finding can assist policymakers and environmental agencies in formulating targeted measures to mitigate PM pollution, such as implementing traffic management strategies and improving urban planning. Moreover, the OLS regression model developed in this study can serve as a useful tool for predicting PM levels based on meteorological conditions and traffic patterns, facilitating proactive pollution control efforts.